关于从超大对接屏幕上重新打出击球的困难
François Sindt1, Guillaume Bret1, Didier Rognan1
1Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France.
Journal of chemical information and modeling
|May 22, 2025
概括
大规模化学图书馆的虚拟选显示出希望,但准确预测绑定亲和力仍然具有挑战性. 选后的分析方法很难从数百万个虚拟命中中药中可靠地识别出强效和多样化的药物候选者.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 化学信息学 化学信息学
背景情况:
- 与传统方法相比,超大虚拟选 (ULVS) 为强效连接体提供了更高的命中率.
- 高效的后处理对于从数百万个虚拟命中中中选择各种化合物进行合成和评估至关重要.
研究的目的:
- 追溯分析各种rescoring方法在ULVS中的绑定亲和度预测的性能.
- 评估能源提炼对不同复合方法的影响.
主要方法:
- 对10个ULVS命中榜单进行分析,与体外结合性试验进行比较.
- 测试了八种重评方法:经验,机器学习,分子力学和量子力学.
- 在重塑之前评估能源提炼的影响.
主要成果:
- 在所有测试中,没有一个单一的恢复方法能够稳定地区分活性化合物和非活性化合物.
- 复杂的方法通常表现更好,但有局限性.
- 能源提炼改善了分子和量子力学,但恶化了经验和机器学习的预测.
结论:
- 目前的复合方法不足以可靠地优先考虑ULVS中的化合物.
- 为了有效的药物发现管道,需要进一步开发强大的结合亲和力预测工具.
- 精炼能源的战略需要仔细考虑,根据所选择的复合方法.
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